1. Technical Field
Example embodiments of the present invention relate in general to a method of analyzing interference between data communication terminals and more specifically to a method of analyzing interference occurring between terminals having different schemes or different business terminals when the terminals use adjacent frequencies.
2. Related Art
Schemes mainly used for analysis of interference between wireless communication systems include a minimum coupling loss (MCL) scheme, a Monte-Carlo scheme, and the like.
First, in the MCL scheme, a minimum distance, i.e., a separation distance or a separation frequency by which a number of systems must be separated to operate without interference between the systems is calculated through a system parameter and a propagation model. An activity factor of a transceiver is neglected and the worst case in which a signal having a certain size is continuously received is assumed. Accordingly, a great separation distance or separation frequency (protection band) not suitable for actual application is calculated.
However, since real systems are normally operated with a much smaller minimum frequency or distance separation compared to results obtained using the MCL scheme, it has been discussed whether the worst result obtained through the MCL scheme is appropriate.
On the other hand, in the Monte-Carlo scheme, all parameter values related to an interference environment are designated and interference probability is statistically calculated. This Monte-Carlo scheme is somewhat complex and has an error in interference probability according to an input parameter value, but has an advantage in that the Monte-Carlo scheme can simulate all interference environments.
FIG. 1 is a conceptual diagram illustrating a situation in which interference between general wireless communication systems occurs.
Referring to FIG. 1, an antenna system that is a target of interference analysis is assumed to be an interfered antenna system 10, and an antenna system causing interference in the interfered antenna system 10 is assumed to be an interfering antenna system 20.
In this case, the interfered antenna system 10 includes an interfered receiver 11 and a party transmitter 12, and the interfering antenna system 20 includes an interfering transmitter 21 and a target receiver 22.
In addition, a strength of a signal that the interfered receiver 11 receives from the party transmitter 12 is defined as a desired receiving signal strength (DRSS), and a strength of a signal that the interfered receiver 11 receives from the interfering transmitter 21 is defined as an interfering receiving signal strength (IRSS). In this case, the signal that the interfered receiver 11 receives from the interfering transmitter 21 causes interference with the interfered receiver 11.
Further, a link established between the interfered receiver 11 and the party transmitter 12 is defined as an interfered link 13, and a link established between interfered receiver 11 and the interfering transmitter 21 is defined as an interfering link 23.
Hereinafter, a method of calculating interference probability of an antenna system using a conventional Monte-Carlo scheme will be described in detail.
First, a parameter corresponding to each of the interfered receiver 11, the interfering transmitter 21, the party transmitter 12, and the target receiver 22 is set. In addition, a parameter of the link between the interfered receiver 11 and the party transmitter 12 and a parameter of the link between the interfering transmitter 21 and the target receiver 22 are also set.
Then, the strength (DRSS) of the signal that the interfered receiver 11 receives from the party transmitter 12 and the strength (IRSS) of the signal that the interfered receiver 11 receives from the interfering transmitter 21 are calculated.
In this case, the strength (DRSS) of the signal that the interfered receiver 11 receives from the party transmitter 12 is expressed as in Equation 1.DRSS=pwtsupplied+gwt→vr−plwt→vr(fvr)+gvr→wt  [Equation 1]
where pwtsupplied denotes a power supplied to the party transmitter 12, gwt→vr denotes an antenna gain directed from the party transmitter 12 to the interfered receiver 11, gvr→wt denotes an antenna gain directed from the interfered receiver 11 to the party transmitter 12, and plwt→vr(fvr) denotes a path loss for a path between the party transmitter 12 and the interfered receiver 11.
In general, interference mechanisms are largely classified into blocking, unwanted emission, and intermod, and IRSS according to the respective interference mechanisms may be expressed as in Equations 2 to 4.IRSSblock,i=(pitsupplied+gitPC+git→vr−plit→vr−avr+gvr→it)  [Equation 2]
where IRSSblock,I denotes a strength of a blocking interfering receiving signal received from an i-th interfering transmitter 21, pit supplied denotes a power supplied to the interfering transmitter 21, gitPC denotes a power control gain for the interfering transmitter 21 in a power control function, git→vr denotes an antenna gain directed from the interfering transmitter 21 to the interfered receiver 11, gvr→it denotes an antenna gain directed from the interfered receiver 11 to the interfering transmitter 21, avr denotes blocking attenuation of the interfered receiver 11, and plit→vr denotes a path loss for a path between the interfering transmitter 21 and the interfered receiver 11.IRSSunwanted—i=(emissionit(fit,fvr)+git→vr−plit→vr(fvr+gvr→it)  [Equation 3]
where IRSSunwanted—i denotes a strength of an interference signal received by the interfered receiver 11 from unwanted emission of the i-th interfering transmitter 21.
Further, emissionit(fit, fvr) denotes a strength of an interference signal received in a receiving bandwidth of the interfered receiver 11. The emissionit(fit, fvr) may be obtained as a function of a generally known unwanted emission mask, a transmitting power strength of the interfering transmitter 21, and the like.Ii,jRSSintermod=2*IiRSSint+IjRSSint−3 intermod−3 sensvr−9dB  [Equation 4]
where Ii,jRSSintermod denotes a strength of an intermod interfering receiving signal received from an i-th interfering transmitter and a j-th interfering transmitter, intermod denotes a 3rd intermod attenuation, and sensvr denotes sensitivity of the interfered receiver.
Accordingly, the interference probability P may be calculated as probability of DRSS/IRSS being equal to or less than a carrier-to-interference ratio (C/I) required in the system under the condition that a DRSS is above a reception sensitivity level.
That is, if a specific parameter among input parameters is input as a range instead of a fixed value, the DRSS and the IRSS are calculated by applying each value in the range and then the number of times DRSS/IRSS does not exceed a threshold (C/I) is divided by a total number of times to obtain interference probability.
This may be expressed as in Equation 5.
                    P        =                  P          ⁢                      {                                                            DRSS                  IRSS                                <                                  C                  I                                            |                              DRSS                >                sens                                      }                                              [                  Equation          ⁢                                          ⁢          5                ]            
This conventional method of calculating interference probability of a wireless communication system using a Monte-Carlo scheme has an advantage in that the method can calculate the interference probability reflecting a real environment. On the other hand, in analysis of interference between data communication terminals, the MCL scheme derives an unreal interference result in consideration of the worst case, and may have a problem in satisfying a necessary protection band or separation distance.
However, there is a problem even when the Monte-Carlo scheme is applied for analysis of interference between terminals, which is that, in the Monte-Carlo scheme, terminals are assumed to be randomly distributed with uniform probability within a radius of a cell.
FIG. 2 is a conceptual diagram illustrating a terminal distribution for analysis of interference between general terminals using a Monte-Carlo scheme.
Referring to FIG. 2, when the Monte-Carlo scheme is applied for analysis of interference between terminals, a situation in which interfered terminals 211 to 216 and interfering terminals 221 to 225 are randomly distributed with uniform probability around a interfered link base station 201 in a radius of one cell 200 is assumed. Accordingly, the interfering terminals are less likely to be distributed near the interfered terminals and analysis of interference between the terminals shows that interference hardly occurs, limiting application of the Monte-Carlo scheme. That is, randomly distributing the terminals within the radius of the cell by applying the Monte-Carlo scheme does not reflect a real terminal use pattern.
For example, in view of a recent hot spot or zone concept, terminals are mainly used in a specific region having a small radius such as an office, coffee shop, subway, or bus. A terminal use pattern in which such specific regions are randomly distributed within a radius of a cell is observed. Accordingly, it is necessary to enhance interference analysis using the Monte-Carlo scheme in consideration of a current terminal use pattern and a cell design scheme.